OpenEnv is a framework for creating, deploying, and using isolated execution environments through simple APIs modeled on Gymnasium. It is intended for researchers and developers training agents with reinforcement learning, as well as creators who need secure environments that can be deployed and accessed through familiar protocols. The catalogue add-ons provide agent integrations and workflows for using OpenEnv.
Borrowing it
Nothing to install: this file belongs to huggingface/OpenEnv. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/huggingface/OpenEnv/main/.claude/skills/work-on-issue/SKILL.mdgit clone --depth 1 https://github.com/huggingface/OpenEnvWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/huggingface/openenv/work-on-issue)<a href="https://agentmods.dev/skills/huggingface/openenv/work-on-issue"><img src="https://agentmods.dev/badge/skills/huggingface/openenv/work-on-issue/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/huggingface/openenv/work-on-issue"><img src="https://agentmods.dev/badge/skills/huggingface/openenv/work-on-issue.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00027 | $0.00787 |
| Opus 5.5 | $0.00011 | $0.00315 |
| Sonnet 5.5 | $0.00005 | $0.00157 |
| Haiku 4.5 | $0.00003 | $0.00079 |
Grade A, and why
work-on-issue scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/work-on-issue
Start focused work on a GitHub issue using TDD workflow.
EXECUTE THESE STEPS NOW
When this skill is invoked, you MUST execute these steps immediately. Do NOT just describe what will happen - actually do it.
Step 1: Parse Issue Number
Extract the issue number from $ARGUMENTS:
- Remove
#prefix if present - The issue number is: $ARGUMENTS
Step 2: Spawn Issue Worker Agent
Use the Task tool to spawn the issue-worker agent:
Task tool:
subagent_type: issue-worker
prompt: "Read GitHub issue #<NUMBER> and extract:
1. Goal - what the user wants to achieve
2. Acceptance criteria - specific testable requirements
3. Edge cases and constraints
4. Suggested PR split if complex"
Wait for the agent to return requirements.
Step 3: Create Worktree
After receiving requirements, run this command:
.claude/scripts/worktree-create.sh issue-<NUMBER>-<short-description>
Where <short-description> is 2-3 words from the goal (e.g., add-mcp-tools).
Step 4: Activate TDD Enforcement
Activate TDD enforcement in the new worktree. This uses tdd-state.sh's
direct-execution mode so it works in a single Bash call:
cd .worktrees/issue-<NUMBER>-<short-description> && bash .claude/hooks/tdd-state.sh activate <NUMBER>
This writes .tdd-session.json to the worktree root, which all hooks check.
Without this step, hooks would not block direct edits.
Step 5: Create Todos
Use TodoWrite to create a todo for EACH acceptance criterion:
TodoWrite:
todos:
- content: "Test: <acceptance criterion 1>"
status: pending
activeForm: "Testing <criterion 1>"
- content: "Test: <acceptance criterion 2>"
status: pending
activeForm: "Testing <criterion 2>"
...
Step 6: Begin TDD Cycle
Immediately invoke /write-tests for the first todo.
DO NOT stop and wait for user input. Start the TDD cycle now.
When to Use
- Starting work on a GitHub issue
- You want TDD enforcement (opt-in via this skill)
- You want isolated work (no branch switching)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 121 lines · 27 tokens per session scan A 12ac3d5a425d
work-on-issue is a skill published in the GitHub repository huggingface/OpenEnv (2,739 stars, last pushed 8d ago), licensed BSD-3-Clause. It adds 27 tokens to every session and 787 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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